Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix
نویسندگان
چکیده
منابع مشابه
Fast Algorithm for Attribute Reduction Based on Rough Set Theory Using Binary Discernibility Matrix
Rough set is a valid mathematical tool that deals with uncertain, vague and incomplete information of decision systems. Attribute reduction is one of the issues in rough set theory, and core attributes are indispensable in the process of attribute reduction. Recently, some researchers proposed the method of binary discernibility matrix to compute the results of attribute reduction, which can no...
متن کاملReduction of Rough Set Based on Generalized Neighborhood System Operator
The theory of generalized neighborhood system-based approximation operators plays an important role in the theory of generalized rough sets since it includes both the neighborhood-based approximation operators and the covering-based approximation operators as its special circumstances. The theory of reduction is one of the most significant directions in rough sets. In this work, the reduction o...
متن کاملA New Symbolic Method for Discernibility Matrix in Rough Set
Generating discernibility matrix consumes huge time and space .To solve this problem, A new Binary Discernibility Matrix (BDM) induced from information table is defined, The concept of Binary Conjunction Matrix(BCM) is then introduced, Finally A novel method for discernibility matrix using Zero-Suppressed BDDs (ZBDD) and Ordered binary decision diagrams (OBDD) is proposed in this paper, experim...
متن کاملFeature Selection by Kernelized Fuzzy Rough Sets for Transient Stability Assessment Based on Gaussian Process
Feature selection of input features is the key issue for pattern recognition-based transient stability assessment (TSA) methods. Considering the possible real-time information provided by phasor measurement units, a group of system-level classification features are firstly extracted from the power system operation condition to construct the original feature set. Then kernelized fuzzy rough sets...
متن کاملNeighborhood rough set based heterogeneous feature subset selection
Feature subset selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Most of researches are focused on dealing with homogeneous feature selection, namely, numerical or categorical features. In this paper, we introduce a neighborhood rough set model to deal with the problem of heterogeneous feature subset selection. As the classical rou...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energies
سال: 2018
ISSN: 1996-1073
DOI: 10.3390/en11010185